The dAIEDGE Network of Excellence (NoE) seeks to strengthen and support the development of a dynamic European cutting-edge Artificial intelligence (AI) ecosystem under the umbrella of the European Lighthouse for AI, and to sustain the development of advanced AI. dAIEDGE fosters the exchange of ideas, concepts, and trends on cutting-edge next generation AI, creating links between ecosystem actors to help both the European Commission (EC) and the European Union (EU) and the peripheral AI constituency identify strategies for future developments in Europe. Our main objective is to advance Europe’s innovation and technology base by developing a comprehensive policy and governance approach to AI in order for the EU to become a world leader in innovation in the data economy and its applications.

Multiple authentication solutions are widely deployed, such as OTP/TOTP/HOTP codes, hardware tokens, PINs, or biometrics. However, in practice, one sometimes needs to authenticate not only the user but also their location. The current state-of-the-art secure localisation schemes are either unreliable or insecure, or require additional hardware to reliably prove the user's location. This paper proposes CARPOOL, a novel, secure, and reliable approach to affirm the location of the user by solely relying on location-bounded interactions with commercial off-the-shelf devices. Our solution does not require any additional hardware, leverages devices already present in a given environment, and can be integrated effortlessly with existing security components, such as identity and access control systems. To demonstrate the feasibility of our work and to show that it can be deployed in a realistic closed environment setting, we implemented a proof of concept realisation of CARPOOL on an Android phone and multiple Raspberry Pi boards and integrated CARPOOL with Amazon Web Services (AWS) Cognito.

At CANS 2024, Zbudila et al. presented MaSTer, a maliciously secure multi-party computation protocol for truncation.It allows adversaries to manipulate outputs with a bounded additive error while avoiding detection with a certain probability.In this work, we analyse the broader implications of adversarial exploitation in probabilistic truncation protocols, specifically in relation to MaSTer.We propose three attack strategies aimed at inducing misclassification in deep neural network (DNN) inference.Our empirical evaluation across multiple datasets demonstrates that while adversarial influence remains negligible under realistic constraints, certain configurations and network architectures exhibit increased vulnerability.By improving the understanding of the risks associated with probabilistic truncation protocols in privacy-preserving machine learning, our work demonstrates that the MaSTer protocol is robust in realistic settings.

This paper introduces an entanglement-based Quantum Distance Bounding (QDB) protocol, whose security derived from Bell’s inequality violation. This protocol leverages the non-locality of quantum entanglement to enhance the integrity and security of quantum channels, providing device-independent assurances. We present both the theoretical framework and a practical implementation scenario using Qiskit. This study lays the groundwork for rigorous future analyses and the refinement of QDB protocols.

Garbling schemes are vital primitives for privacy-preserving protocols and secure two-party computation. This paper presents a projective garbling scheme that assigns$2^{n}$values to wires in a circuit comprising XOR and unary projection gates. A generalization of FreeXOR allows the XOR of wires with$2^{n}$values to be very efficient. We then analyze the performance of our scheme by evaluating substitution-permutation ciphers. Using our proposal, we measure high-speed evaluation of the ciphers with a moderately increased cost in garbling and bandwidth. Theoretical analysis suggests that for evaluating the nine examined ciphers, one can expect a 4- to 70-fold improvement in evaluation performance with, at most, a 4-fold increase in garbling cost and, at most, an 8-fold increase in communication cost compared to the Half-Gates (Zahur, Rosulek and Evans; Eurocrypt’15) and ThreeHalves (Rosulek and Roy; Crypto’21) garbling schemes. In an offline/online setting, such as secure function evaluation as a service, the circuit garbling and communication to the evaluator can proceed in the offline phase. Thus, our scheme offers a fast online phase. Furthermore, we present efficient Boolean circuits for the S-boxes of TWINE and Midori64 ciphers. To our knowledge, our formulas give the smallest number of AND gates for the S-boxes of these two ciphers.

IoT devices collect privacy-sensitive data, e.g., in smart grids or in medical devices, and send this data to cloud servers for further processing. In order to ensure confidentiality as well as authenticity of the sensor data in the untrusted cloud environment, we consider a transciphering scenario between embedded IoT devices and multiple cloud servers that perform secure multi-party computation (MPC). Concretely, the IoT devices encrypt their data with a lightweight symmetric cipher and send the ciphertext to the cloud servers. To obtain the secret shares of the cleartext message for further processing, the cloud servers engage in an MPC protocol to decrypt the ciphertext in a distributed manner. This way, the plaintext is never exposed to the individual servers.

We propose, design, and evaluate PIVOT, a privacy-enhancing and effective contact tracing solution that aims to strike a balance between utility and privacy: one that does not collect sensitive information yet allowing effective tracing and notifying the close contacts of diagnosed users. PIVOT requires a considerably low degree of trust in the entities involved compared to centralized alternatives while retaining the necessary utility. To protect users’ privacy, it uses local proximity tracing based on broadcasting and recording constantly changing anonymous public keys via short-range communication. These public keys are used to establish a shared secret key between two people in close contact. The three keys (i.e., the two public keys and the established shared key) are then used to generate two unique per-user-per-contact hashes: one for infection registration and one for exposure score query. These hashes are never revealed to the public. To improve utility, user exposure score computation is performed centrally, which provides health authorities with minimal, yet insightful and actionable data. Data minimization is achieved by the use of per-user-per-contact hashes and by enforcing role separation: the health authority act as a mixing node, while the matching between reported and queried hashes is outsourced to a third entity, an independent matching service (MS). This separation ensures that out-of-scope information, such as users’ social interactions, is hidden from the health authorities, whereas the MS does not learn users’ sensitive information. To sustain our claims, we conduct a practical evaluation that encompasses anonymity guarantees and energy requirements.

We present a distance bounding (DB) protocol that employs quantum-bits (qubits) in the implementation of the rapid-bit exchange (a.k.a., challenge-response) phase, instead of classical bits. This is not the first attempt at using qubits in a DB protocol, as Abidin et al. proposed a way to implement DB protocols using qubits in RFIDSec 2016 [1]. We improve upon Abidin et al.'s protocol and make it resistant to photon number splitting attacks. In addition, our new protocol eliminates the need for communication from the prover to the verifier in the last authentication phase, which is necessary in the protocol by Abidin et al.

The procedure for extracting a cryptographic key from noisy sources, such as biometrics and physically uncloneable functions (PUFs), is known as fuzzy extractor (FE). Although FE constructions deal with discrete sources, most noisy sources are continuous. In the continuous case, it is required to transform the source to a discrete one. We introduce a 1) model-based uncoupling construction that directly deals with the continuous noisy source and produces helper data uncoupling the discrete representation from the noisy source, guaranteeing the diversity of the discrete representation, and making it more robust and a 2) strengthened uncoupled fuzzy extractor, suitable for privacy-preserving applications, which integrates an additional fixed authentication factor and obtains a key uncoupled to the noisy sources and unlinkable helper data. We present optimal model-based uncoupling constructions for Gaussian sources. Specifically, we show how to: 1) extract one or multiple bits from a single Gaussian source; 2) extract one bit from several unreliable Gaussian sources; and 3) provide a general procedure to obtain an optimal uncoupled FE from Gaussian source(s). Our experiments show that the proposed constructions achieve much higher security levels for wide operational scenarios, approximately doubling the obtained effective key length without affecting false rejection rates.

Biometrics are widely used for authentication in several domains, services and applications. However, only very few systems succeed in effectively combining highly secure user authentication with an adequate privacy protection of the biometric templates, due to the difficulty associated with jointly providing good authentication performance, unlinkability and irreversibility to biometric templates. This thwarts the use of biometrics in remote authentication scenarios, despite the advantages that this kind of architectures provides. We propose a user-specific approach for decoupling the biometrics from their binary representation before using biometric protection schemes based on fuzzy extractors. This allows for more reliable, flexible, irreversible and unlinkable protected biometric templates. With the proposed biometrics decoupling procedures, biometric metadata, that does not allow to recover the original biometric template, is generated. However, different biometric metadata that are generated starting from the same biometric template remain statistically linkable, therefore we propose to additionally protect these using a second authentication factor (e.g., knowledge or possession based). We demonstrate the potential of this approach within a two-factor authentication protocol for remote biometric authentication in mobile scenarios.